Enterprise risk prediction method, early warning system and equipment

Abstract

The present invention relates to the field of risk prediction technology, and in particular to an enterprise risk prediction method, early warning system and equipment. The method comprises the following steps: collecting enterprise historical data, including financial data, market data, operational data and external environment data; preprocessing the collected historical data, including data cleaning, data normalization and data dimension reduction; constructing an enterprise risk assessment model based on the preprocessed data, wherein the model adopts a machine learning algorithm, including a random forest algorithm, a support vector machine algorithm and a neural network algorithm; using the risk prediction model to conduct risk assessment on the current enterprise data and generate risk assessment results; generating enterprise risk early warning signals based on the risk assessment results, and providing corresponding risk response measures and suggestions. The present invention realizes intelligent and precise prediction and early warning of enterprise risks, provides a powerful decision support tool for enterprise decision makers, and helps enterprises achieve steady development.

Description

Technical Field

[0001] The present invention relates to the technical field of risk prediction, and in particular to an enterprise risk prediction method, early warning system and equipment. Background Art

[0002] In today's complex and ever-changing market environment, enterprises are faced with a variety of internal and external risk challenges. These risks not only affect the financial status of enterprises, but may also have a profound impact on the market position, brand image and long-term development potential of enterprises. Therefore, how to accurately and timely predict and evaluate enterprise risks has become an important issue that enterprise managers need to solve urgently.

[0003] Traditional enterprise risk management methods often rely on manual analysis and expert judgment, which is not only time-consuming and labor-intensive, but also difficult to fully cover all potential risks. With the rapid development of technologies such as big data, cloud computing, and artificial intelligence, new opportunities have emerged in enterprise risk prediction and management. By building an enterprise risk prediction model based on big data and machine learning algorithms, it is possible to achieve automated processing and intelligent analysis of massive data, thereby improving the accuracy and timeliness of risk prediction.

[0004] However, existing enterprise risk prediction methods and technologies still have certain limitations. On the one hand, some methods only focus on a single financial indicator or market data, ignoring the comprehensive impact of multi-dimensional factors such as enterprise operations and external environment; on the other hand, some methods face problems such as large computational complexity and poor model generalization ability when processing complex data and high-dimensional features, making it difficult to achieve good results in practical applications.

[0005] Therefore, it is necessary to develop a more comprehensive, efficient and intelligent enterprise risk prediction method, early warning system and equipment to meet the urgent needs of enterprises for risk management in a complex market environment. Summary of the invention

[0006] The present invention provides an enterprise risk prediction method, early warning system and equipment, which realize intelligent and accurate prediction and early warning of enterprise risks, provide enterprise decision makers with a powerful decision support tool, and help enterprises achieve stable development.

[0007] The technical solution adopted by the present invention is: a method for predicting enterprise risks, comprising the following steps:

[0008] Step 1: Collect historical data of the enterprise, including financial data, market data, operational data and external environment data;

[0009] Step 2: Preprocess the collected historical data, including data cleaning, data normalization and data dimension reduction;

[0010] Step 3: Based on the preprocessed data, construct an enterprise risk assessment model, wherein the model adopts a machine learning algorithm, including a random forest algorithm, a support vector machine algorithm, and a neural network algorithm;

[0011] Step 4: Use the risk prediction model to conduct risk assessment on current enterprise data and generate risk assessment results;

[0012] Step 5: Generate enterprise risk warning signals based on risk assessment results and provide corresponding risk response measures.

[0013] The key indicators in the company's historical data include operating income growth rate, profit margin, debt-to-asset ratio, cash flow, market share changes, customer satisfaction, employee turnover rate, and changes in policies and regulations.

[0014] The enterprise risk warning signal sets different risk thresholds and divides the enterprise risk level into multiple levels, including low risk, medium risk and high risk. For the low risk level, it is recommended that the enterprise maintain the status quo and continue to monitor; for the medium risk level, it is recommended that the enterprise take preventive measures, including adjusting business strategies and strengthening internal controls; for the high risk level, it is recommended that the enterprise take immediate response measures, including raising funds, restructuring businesses, and seeking external support.

[0015] An enterprise risk warning system consists of a data acquisition module, a data processing module, a risk prediction module, a warning generation module, and a user interaction module. The data acquisition module is used to collect historical data and current data of the enterprise; the data processing module is used to preprocess the collected data; the risk prediction module builds an enterprise risk prediction model based on the preprocessed data, and performs risk assessment on the current enterprise data; the warning generation module generates an enterprise risk warning signal according to the risk assessment result, and provides corresponding risk response measures suggestions; the user interaction module is used to display risk warning signals and risk response measures suggestions for users to query and operate.

[0016] The data acquisition module includes a data interface for exchanging data with the enterprise's internal system and external data sources to ensure the comprehensiveness and real-time nature of the data.

[0017] The data processing module includes a data cleaning submodule, a data normalization submodule, and a data dimension reduction submodule. The data cleaning submodule is to remove outliers and missing values, identify and process potential noise data; the data normalization submodule is to eliminate the impact of different dimensions and orders of magnitude to ensure the accuracy of model training; the data dimension reduction submodule reduces the complexity of the data through the principal component analysis method, and retains key information to improve the operational efficiency of the model.

[0018] The risk prediction module includes a model training submodule and a model evaluation submodule. The model training submodule uses historical data to train an enterprise risk assessment model through a machine learning algorithm; the model evaluation submodule uses an independent test data set to verify the trained model, evaluate its prediction accuracy and generalization ability, and ensure the effectiveness of the model in practical applications.

[0019] The user interaction module provides an intuitive graphical interface and interaction mode, and supports user-defined query, report generation and decision support functions.

[0020] An enterprise risk prediction device includes a processor, a memory, an input-output device, and a network interface. The processor is used to execute program instructions stored in the memory to realize the functions of the above-mentioned enterprise risk prediction method; the memory is used to store enterprise historical data, risk assessment models, risk assessment results, and relevant data of risk warning signals; the input-output device includes a display screen, a keyboard, and a mouse, which are used to interact with users, display risk warning information and receive user input; the network interface is used to exchange data with an internal network or an external network of the enterprise to realize remote monitoring and real-time warning.

[0021] Beneficial effects of the present invention:

[0022] (1) Improving prediction accuracy and comprehensiveness: The present invention achieves a comprehensive and in-depth insight into corporate risks by integrating multi-dimensional information such as financial data, market data, operational data, and external environmental data. Compared with traditional methods, this multi-source data fusion method can more comprehensively reflect the operating status of the company and changes in the external environment, thereby significantly improving the accuracy of risk prediction. At the same time, by utilizing the self-learning ability of machine learning algorithms, the model can continuously absorb knowledge from new data and optimize itself to cope with complex and changing market environments, ensuring the timeliness and accuracy of prediction results.

[0023] (2) Enhance the real-time and intelligent nature of the early warning system: The present invention ensures the instant capture and feedback of risk information through real-time data collection and processing. The system's built-in machine learning model can quickly process and analyze massive amounts of data and automatically generate early warning signals when potential risks are discovered, greatly shortening the risk response time. In addition, the system also has an intelligent recommendation function that can automatically match corresponding risk response measures based on risk assessment results, providing enterprise managers with instant decision support and helping enterprises quickly respond to risk challenges.

[0024] (3) Optimizing user experience and decision-making efficiency: The present invention adopts an intuitive and easy-to-use graphical interface and a flexible interactive method, which lowers the user operation threshold and makes it easy for non-professionals to get started. Through customized query, report generation and other functions, users can easily obtain the required risk information and provide more comprehensive and accurate data support for decision-making. At the same time, the system's remote monitoring and real-time early warning functions enable enterprise managers to grasp the enterprise's risk status at any time no matter where they are, effectively improving decision-making efficiency and enterprise management level.

[0025] (4) Promote the modernization of enterprise risk management: The enterprise risk prediction method, early warning system and equipment of the present invention not only provide enterprises with an efficient and intelligent risk management tool, but also promote the modernization of enterprise risk management concepts; by introducing advanced technologies such as big data, cloud computing, and artificial intelligence, enterprises can achieve dynamic monitoring, accurate prediction and scientific response to risks, thereby building a scientific, standardized and efficient risk management system; this not only helps to enhance the company's own risk resistance, but also promotes the healthy and stable development of the entire industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a block diagram of an enterprise risk early warning system of an enterprise risk prediction method, early warning system and equipment of the present invention;

[0027] Figure 2 The present invention discloses an enterprise risk prediction method, an early warning system and an enterprise risk prediction method flow chart of the device. DETAILED DESCRIPTION

[0028] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] The present invention provides a method for predicting enterprise risks, comprising the following steps:

[0030] Step 1: Collect historical data of the enterprise, including financial data, market data, operational data and external environment data;

[0031] Step 2: Preprocess the collected historical data, including data cleaning, data normalization and data dimension reduction;

[0032] Step 3: Based on the preprocessed data, build an enterprise risk assessment model using machine learning algorithms, including random forest algorithm, support vector machine algorithm, and neural network algorithm;

[0033] Step 4: Use the risk prediction model to conduct risk assessment on current enterprise data and generate risk assessment results;

[0034] Step 5: Generate enterprise risk warning signals based on risk assessment results and provide corresponding risk response measures.

[0035] The key indicators in the enterprise historical data in the present invention include operating income growth rate, profit margin, debt-to-asset ratio, cash flow, market share changes, customer satisfaction, employee turnover rate and changes in policies and regulations.

[0036] The enterprise risk warning signal in the present invention divides the enterprise risk level into multiple levels, including low risk, medium risk and high risk, by setting different risk thresholds. For the low risk level, it is recommended that the enterprise maintain the status quo and continue to monitor; for the medium risk level, it is recommended that the enterprise take preventive measures, including adjusting business strategies and strengthening internal controls; for the high risk level, it is recommended that the enterprise take immediate response measures, including fund raising, business restructuring, and seeking external support.

[0037] An enterprise risk early warning system consists of a data acquisition module, a data processing module, a risk prediction module, an early warning generation module, and a user interaction module. The data acquisition module is used to collect historical data and current data of the enterprise; the data processing module is used to preprocess the collected data; the risk prediction module builds an enterprise risk prediction model based on the preprocessed data, and performs risk assessment on the current enterprise data; the early warning generation module generates an enterprise risk early warning signal according to the risk assessment result, and provides corresponding risk response measures suggestions; the user interaction module is used to display risk warning signals and risk response measures suggestions for users to query and operate.

[0038] The data acquisition module in the present invention includes a data interface for exchanging data with the internal system of the enterprise and the external data source to ensure the comprehensiveness and real-time nature of the data.

[0039] The data processing module in the present invention includes a data cleaning submodule, a data normalization submodule, and a data dimension reduction submodule. The data cleaning submodule is to remove outliers and missing values, identify and process potential noise data; the data normalization submodule is to eliminate the influence of different dimensions and orders of magnitude to ensure the accuracy of model training; the data dimension reduction submodule reduces the complexity of the data through the principal component analysis method, and retains key information to improve the operational efficiency of the model.

[0040] The risk prediction module in the present invention includes a model training submodule and a model evaluation submodule. The model training submodule uses historical data to train an enterprise risk assessment model through a machine learning algorithm; the model evaluation submodule uses an independent test data set to verify the trained model, evaluate its prediction accuracy and generalization ability, and ensure the effectiveness of the model in practical applications.

[0041] The user interaction module in the present invention provides an intuitive graphical interface and interaction mode, and supports user-defined query, report generation and decision support functions.

[0042] An enterprise risk prediction device includes a processor, a memory, an input / output device, and a network interface. The processor is used to execute program instructions stored in the memory to realize the functions of the above-mentioned enterprise risk prediction method; the memory is used to store enterprise historical data, risk assessment models, risk assessment results, and relevant data of risk warning signals; the input / output device includes a display screen, a keyboard, and a mouse, which are used to interact with users, display risk warning information, and receive user input; the network interface is used to exchange data with an internal network or an external network of the enterprise to realize remote monitoring and real-time warning.

[0043] Embodiment 1:

[0044] This embodiment is applied to a medium-sized manufacturing enterprise. In recent years, it has faced challenges such as intensified market competition, large fluctuations in raw material prices, and changing customer needs. It urgently needs an efficient risk management system to cope with it. The enterprise decided to adopt the enterprise risk prediction method, early warning system and equipment described in the present invention to improve its risk management capabilities.

[0045] (I) Data collection and preprocessing: First, the data acquisition module automatically captures historical data from the company's ERP system, CRM system, financial system and other internal data sources through the preset data interface, including financial data (such as operating income growth rate, profit margin, debt-to-asset ratio, etc.), market data (such as market share changes, customer satisfaction, etc.), operational data (such as employee turnover, production efficiency, etc.) and external environmental data (such as policy and regulatory changes, industry trends, etc.) for the past five years. Subsequently, the data processing module cleans these data, removes outliers and missing values, performs normalization to eliminate dimensionality effects, and performs data dimensionality reduction through principal component analysis to retain key information and reduce computational complexity.

[0046] (II) Risk assessment model construction: Based on the preprocessed data, the model training submodule of the risk prediction module uses a variety of machine learning algorithms such as random forest algorithm, support vector machine algorithm and neural network algorithm to build an enterprise risk assessment model. Through cross-validation and parameter tuning, the optimal model parameters are selected to ensure the prediction accuracy and generalization ability of the model. The model evaluation submodule uses an independent test data set to verify the trained model and evaluate its effect in practical applications.

[0047] (III) Risk assessment and early warning: In daily operations, the data collection module continuously collects the current data of the enterprise and transmits it to the risk prediction module. The risk prediction module uses the trained risk assessment model to evaluate the current data and generate risk assessment results. The early warning generation module converts the assessment results into risk warning signals such as "low risk", "medium risk" or "high risk" according to the set risk threshold, and automatically generates corresponding risk response measures. These early warning signals and response measures are displayed to enterprise managers through the user interaction module in an intuitive graphical interface, helping them to quickly understand the enterprise risk status and make decisions.

[0048] (IV) Decision support and continuous optimization: After enterprise managers view risk warning signals and response measures through the user interaction module, they can adjust or adopt them based on their own experience and actual situation. At the same time, the user interaction module also supports custom query and report generation functions to help managers gain a deeper understanding of risk sources and changing trends. As the enterprise continues to operate, new data is constantly generated and incorporated into the risk assessment model. The model continuously improves prediction accuracy and adaptability through self-learning and optimization.

[0049] Implementation effect: After adopting the present invention, the risk management level of the manufacturing enterprise has been significantly improved. Enterprise managers can grasp the enterprise risk status in real time and make scientific decisions; the timeliness and accuracy of the risk warning system effectively reduce the losses suffered by the enterprise due to risks; the custom query and report generation functions provide managers with comprehensive data support; at the same time, the system's remote monitoring and real-time warning functions enable managers to grasp the dynamics of the enterprise at any time no matter where they are. In addition, by introducing advanced technologies such as big data, cloud computing and artificial intelligence, the enterprise has built a scientific, standardized and efficient risk management system, laying a solid foundation for the sustainable development of the enterprise.

[0050] Embodiment 2:

[0051] This embodiment is applied to a large financial service enterprise operating in the financial market. The enterprise has a wide range of business, involving multiple fields such as banking, insurance, and securities, and faces multiple challenges such as market risk, credit risk, and operational risk. In order to enhance risk resistance and improve business operation efficiency, the enterprise decided to introduce the enterprise risk prediction method, early warning system, and equipment described in the present invention to build a more intelligent and efficient risk management system.

[0052] (I) Data integration and preprocessing: Considering the diversity and complexity of data in financial services companies, the data collection module first integrates multiple data sources, including internal transaction systems, customer management systems, risk management systems, and external financial market data, macroeconomic data, etc. Through efficient data interfaces and data processing processes, the module realizes real-time data capture and batch processing. Subsequently, the data processing module uses more sophisticated data cleaning strategies to identify and process potential fraudulent transaction records, abnormal fluctuation data, etc. based on the characteristics of financial data. At the same time, time series analysis and other methods are used to normalize and extract features from financial data to ensure that the data quality meets the requirements of the risk assessment model.

[0053] (ii) Customization of risk assessment models: In view of the uniqueness of risks in financial services companies, the model training submodule of the risk prediction module takes into account the particularity and complexity of the financial industry when constructing risk assessment models. In addition to conventional machine learning algorithms, deep learning technologies such as LSTM (Long Short-Term Memory Network) and Transformer models are introduced to capture long-term dependencies and complex patterns in financial time series data. By combining multiple algorithms and model fusion technologies, a customized risk assessment model for the company's specific risk scenarios is constructed. The model evaluation submodule uses rigorous cross-validation and sensitivity analysis to ensure the stability and reliability of the model under different market environments and risk types.

[0054] (III) Dynamic risk monitoring and early warning: In daily operations, the data collection module keeps pace with the real-time data of the financial market to ensure the timeliness and accuracy of risk assessment. The risk prediction module uses the trained risk assessment model to quickly evaluate the real-time data and generate dynamic risk early warning signals. The early warning generation module automatically adjusts the early warning level according to the severity and urgency of the early warning signal and triggers the corresponding risk response process. At the same time, the system also provides customized risk reports and visual analysis tools to help corporate management deeply understand the risk distribution and changing trends, and provide strong support for decision-making.

[0055] (IV) Intelligent decision-making and continuous optimization: Through the user interaction module, the management of financial service companies can intuitively view risk warning information, risk reports and visual analysis results. The system also supports intelligent decision-making support functions, providing management with the best risk response strategies and resource allocation plans through algorithm recommendations and simulation analysis. In addition, with the continuous influx of new data and the continuous learning and optimization of the model, the accuracy and adaptability of the risk prediction system will continue to improve. Through regular performance evaluation and model updates, ensure that the system is always in the best condition to safeguard the stable operation of the enterprise.

[0056] Implementation effect: After the introduction of the present invention, the risk management capability of the financial service enterprise has been significantly improved. The dynamic risk monitoring and early warning system enables the enterprise to timely discover and respond to potential risks, effectively reducing the possibility of losses. The intelligent decision support function provides a scientific decision-making basis for the management, improving business operation efficiency and market competitiveness. At the same time, the continuous optimization and upgrading of the system ensures that the enterprise can keep up with the pace of changes in the financial market and maintain the foresight and effectiveness of risk management. By building an intelligent and efficient risk management system, the financial service enterprise has laid a solid foundation for its sustainable development.

[0057] To sum up, the enterprise risk prediction method, early warning system and equipment of the present invention have significant advantages in improving prediction accuracy and comprehensiveness, enhancing the real-time and intelligence of the early warning system, optimizing user experience and decision-making efficiency, and promoting the modernization of enterprise risk management, providing a strong guarantee for the steady development of enterprises.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting enterprise risks, characterized in that: The following steps are involved: Step 1: Collect historical data of the enterprise, including financial data, market data, operational data and external environment data; Step 2: Preprocess the collected historical data, including data cleaning, data normalization and data dimension reduction; Step 3: Based on the preprocessed data, construct an enterprise risk assessment model, wherein the model adopts a machine learning algorithm, including a random forest algorithm, a support vector machine algorithm, and a neural network algorithm; Step 4: Use the risk prediction model to conduct risk assessment on current enterprise data and generate risk assessment results; Step 5: Generate enterprise risk warning signals based on risk assessment results and provide corresponding risk response measures.

2. The enterprise risk prediction method according to claim 1, characterized in that: The key indicators in the company's historical data include operating income growth rate, profit margin, debt-to-asset ratio, cash flow, market share changes, customer satisfaction, employee turnover rate, and changes in policies and regulations.

3. The enterprise risk prediction method according to claim 1, characterized in that: The enterprise risk warning signal sets different risk thresholds to classify enterprise risk levels into multiple levels, including low risk, medium risk, and high risk. For low risk levels, enterprises are advised to maintain the status quo and continue monitoring; For medium risk levels, companies are advised to take preventive measures, including adjusting business strategies and strengthening internal controls; for high risk levels, companies are advised to take immediate response measures, including raising funds, restructuring businesses, and seeking external support.

4. An enterprise risk early warning system, characterized in that: It consists of a data acquisition module, a data processing module, a risk prediction module, an early warning generation module, and a user interaction module. The data acquisition module is used to collect historical data and current data of the enterprise; The data processing module is used to pre-process the collected data; The risk prediction module constructs an enterprise risk prediction model based on the preprocessed data and performs risk assessment on the current enterprise data; The warning generation module generates enterprise risk warning signals based on risk assessment results and provides corresponding risk response measures suggestions; The user interaction module is used to display risk warning signals and risk response measures suggestions to facilitate user inquiries and operations.

5. The enterprise risk early warning system according to claim 4, characterized in that: The data acquisition module includes a data interface for exchanging data with the enterprise's internal system and external data sources to ensure the comprehensiveness and real-time nature of the data.

6. The enterprise risk early warning system according to claim 4, characterized in that: The data processing module includes a data cleaning submodule, a data normalization submodule, and a data dimension reduction submodule. The data cleaning submodule is to remove outliers and missing values, identify and process potential noise data; the data normalization submodule is to eliminate the impact of different dimensions and orders of magnitude to ensure the accuracy of model training; the data dimension reduction submodule reduces the complexity of the data through the principal component analysis method, and retains key information to improve the operational efficiency of the model.

7. The enterprise risk early warning system according to claim 4, characterized in that: The risk prediction module includes a model training submodule and a model evaluation submodule. The model training submodule uses historical data to train an enterprise risk assessment model through a machine learning algorithm; the model evaluation submodule uses an independent test data set to verify the trained model, evaluate its prediction accuracy and generalization ability, and ensure the effectiveness of the model in practical applications.

8. The enterprise risk early warning system according to claim 4, characterized in that: The user interaction module provides an intuitive graphical interface and interaction mode, and supports user-defined query, report generation and decision support functions.

9. An enterprise risk prediction device, characterized in that: It includes a processor, a memory, an input and output device, and a network interface. The processor is used to execute program instructions stored in the memory to realize the functions of the above-mentioned enterprise risk prediction method; the memory is used to store enterprise historical data, risk assessment models, risk assessment results, and relevant data of risk warning signals; The input and output devices include a display screen, a keyboard and a mouse, which are used to interact with users, display risk warning information and receive user input; the network interface is used to exchange data with the enterprise's internal network or external network to achieve remote monitoring and real-time warning.

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